class_docstr: Computes Binary Cross Entropy (BCE) loss. The BCELoss class expects output from Sigmoid function.
docstr: Output types definitions for binary cross entropy loss. Weights for labels can be set using weight variables.
docstr_tok: ["Output","types","definitions","for","binary","cross","entropy","loss",".","Weights","for","labels","can","be","set","using","weight","variables","."]
code: def output_types(self):
      """
      Output types definitions for binary cross entropy loss. Weights for labels can be set using weight variables.
      """
      return {"loss": NeuralType(elements_type=LossType())}
  
file_path: NeMo/nemo/collections/asr/losses/bce_loss.py
imports: import torch
  from nemo.core.classes import Loss, Typing, typecheck
  from nemo.core.neural_types import LabelsType, LengthsType, LossType, NeuralType, ProbsType
label_desc: Use image node_id 2 for calling the BCELoss obj's underlying member method code with example usage: obj.output_types() and returns: dict